Abstract
Zero-shot learning, a special case of unsupervised domain adaptation where the source and target domains have
disjoint label spaces, has become increasingly popular in
the computer vision community. In this paper, we propose
a novel zero-shot learning method based on discriminative
sparse non-negative matrix factorization. The proposed approach aims to identify a set of common high-level semantic components across the two domains via non-negative
sparse matrix factorization, while enforcing the representation vectors of the images in this common component-based
space to be discriminatively aligned with the attributebased label representation vectors. To fully exploit the
aligned semantic information contained in the learned representation vectors of the instances, we develop a label
propagation based testing procedure to classify the unlabeled instances from the unseen classes in the target domain. We conduct experiments on four standard zero-shot
learning image datasets, by comparing the proposed approach to the state-of-the-art zero-shot learning methods.
The empirical results demonstrate the efficacy of the proposed approach